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A Survey of Machine Learning-Based Resource Scheduling Algorithms in Cloud Computing Environment

机译:云计算环境中基于机器学习资源调度算法的调查

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As a new type of computing resource, cloud computing attracts more and more users because it is convenient and quick service. The cloud server is used by a large number of users, which brings about the problem of how to reasonably schedule resources to ensure the load balance of the cloud environment. With the development of research, scholars have found that the simple job scheduling of physical resources cannot meet the utilization of resources. Connecting the characteristic of resource scheduling in cloud environment and machine learning, researchers gradually abstract a resource scheduling problem into a mathematical problem, and then combine machine learning with group algorithm to put forward the intelligent algorithm which can optimize the resource structure and the improve the resource utilization. In this survey, we discuss several algorithms that use machine learning to solve resource scheduling problems in a cloud environment. Experiments show that machine learning can assist the cloud environment to achieve load balancing.
机译:作为一种新型的计算资源,云计算吸引了越来越多的用户,因为它是方便快捷的服务。云服务器由大量用户使用,这将带来如何合理地安排资源以确保云环境的负载余额的问题。随着研究的发展,学者们发现物理资源的简单工作计划无法满足资源的利用率。连接云环境和机器学习中资源调度的特征,研究人员逐步摘要一个资源调度问题到数学问题中,然后将机器学习与组算法相结合,提出了可以优化资源结构和改进资源的智能算法利用率。在本调查中,我们讨论了几种使用机器学习的算法来解决云环境中的资源调度问题。实验表明,机器学习可以帮助云环境实现负载平衡。

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